AIMC Topic: Diagnosis, Computer-Assisted

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Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning.

Journal of digital imaging
The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by t...

ASSESSMENT OF CENTRAL SEROUS CHORIORETINOPATHY DEPICTED ON COLOR FUNDUS PHOTOGRAPHS USING DEEP LEARNING.

Retina (Philadelphia, Pa.)
PURPOSE: To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology.

Classifying Pneumonia among Chest X-Rays Using Transfer Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Chest radiography has become the modality of choice for diagnosing pneumonia. However, analyzing chest X-ray images may be tedious, time-consuming and requiring expert knowledge that might not be available in less-developed regions. therefore, comput...

Lung Region Segmentation in Chest X-Ray Images using Deep Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Lung cancer is, by far, the leading cause of cancer death in the world. Tools for automated medical imaging analysis development of a Computer-Aided Diagnosis method comprises several tasks. In general, the first one is the segmentation of region of ...

Dense-Unet: a light model for lung fields segmentation in Chest X-Ray images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic and accurate lung segmentation in chest X-ray (CXR) images is fundamental for computer-aided diagnosis systems since the lung is the region of interest in many diseases and also it can reveal useful information by its contours. While deep l...

Artificial intelligence: a disruptive tool for a smarter medicine.

European review for medical and pharmacological sciences
OBJECTIVE: Although highly successful, the medical R&D model is failing at improving people's health due to a series of flaws and defects inherent to the model itself. A new collective intelligence, incorporating human and artificial intelligence (AI...

Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study.

Medicine
Convolutional neural networks (CNNs), a particular type of deep learning architecture, are positioned to become one of the most transformative technologies for medical applications. The aim of the current study was to evaluate the efficacy of deep CN...

Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images.

Applied optics
Breast cancer accounts for the highest number of female deaths worldwide. Early detection of the disease is essential to increase the chances of treatment and cure of patients. Infrared thermography has emerged as a promising technique for diagnosis ...

Computer-aided diagnosis in the era of deep learning.

Medical physics
Computer-aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then be used to...